Aims: The aim of this unit is to provide students with the knowledge necessary to analyse macro/time series data. Both univariate and multivariate models are considered with and without the stationary assumption.
Learning Outcomes:
The learning outcomes of the unit are for students to (1) develop a comprehensive set of tools and techniques for analysing various forms of univariate and multivariate time series and for understanding the current literature in applied time series econometrics; (2) survey the current research topics in time series econometrics and be critically aware of how the theoretical results are used and applied in practice; (3) be able to undertake their own (time series) econometric exercises.
Skills:
Ability to develop rigorous arguments through precise use of concepts and mathematical models (Taught/Facilitated/Assessed).
Ability to select, analyse and present numerical data using econometric packages (T/F/A).
Ability to select, summarise and synthesis written information from multiple sources (T/F/A).
Ability to select and use appropriate ideas to produce a coherent response to a pre-set question (T/F/A).
Comprehensive and scholarly written communication (T/F/A).
Concise and effective written communication (e.g. briefings / written exams) (T/F/A).
Effective oral communication (e.g. lecture question and answer) (F).
Ability to formulate a research question, then develop and present an original & coherent answer (T/F/A).
Ability to produce work to agreed specifications and deadlines (T/F/A).
Content:
The unit begins with stationary univariate models by explaining the theory of difference equations, demonstrating that they are the foundation of all time-series models. The stationary univariate analysis emphasises on the ARMA models and Box-Jenkins methodology. The unit focuses on univariate and multivariate models with and without the stationary assumption. Many recent developments in time series analysis including ARIMA models, unit root tests, cointegration/error-correction models, vector autoregressions and TAR, M-TAR models are considered. There will be numerous examples to illustrate the various techniques, many of which concern models of macroeconomics, finance, international trade and agricultural economics.
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